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add whisperx support

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This commit is contained in:
Mariano Gabriel
2025-12-03 06:48:45 -03:00
parent 7b919beda6
commit 7d7ec15ff7
4 changed files with 87 additions and 16 deletions
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2
meetus/cache_manager.py
View File

@@ -73,7 +73,7 @@ class CacheManager:
if not self.use_cache or self.skip_cache_frames or not self.frames_dir.exists():
return None
existing_frames = list(self.frames_dir.glob("frame_*.jpg"))
existing_frames = list(self.frames_dir.glob("*.jpg"))
if not existing_frames:
return None

50
meetus/transcript_merger.py
View File

@@ -70,8 +70,10 @@ class TranscriptMerger:
for seg in data
]
# Group by interval if requested
if group_interval and segments:
# Group by interval if requested, but skip if we have speaker diarization
# (merge_transcripts will group by speaker instead)
has_speakers = any(seg.get('speaker') for seg in segments)
if group_interval and segments and not has_speakers:
segments = self.group_audio_by_intervals(segments, group_interval)
return segments
@@ -164,13 +166,14 @@ class TranscriptMerger:
) -> List[Dict]:
"""
Merge audio and screen transcripts by timestamp.
Groups consecutive audio from same speaker until a screen frame interrupts.
Args:
audio_segments: List of audio transcript segments
screen_segments: List of screen OCR segments
Returns:
Merged list sorted by timestamp
Merged list sorted by timestamp, with audio grouped by speaker
"""
# Mark segment types
for seg in audio_segments:
@@ -182,7 +185,46 @@ class TranscriptMerger:
all_segments = audio_segments + screen_segments
all_segments.sort(key=lambda x: x['timestamp'])
return all_segments
# Group consecutive audio segments by speaker (screen frames break groups)
grouped = []
current_group = None
for seg in all_segments:
if seg['type'] == 'screen':
# Screen frame: flush current group and add frame
if current_group:
grouped.append(current_group)
current_group = None
grouped.append(seg)
else:
# Audio segment
speaker = seg.get('speaker')
if current_group is None:
# Start new group
current_group = {
'timestamp': seg['timestamp'],
'text': seg['text'],
'speaker': speaker,
'type': 'audio'
}
elif speaker == current_group.get('speaker'):
# Same speaker, append text
current_group['text'] += ' ' + seg['text']
else:
# Speaker changed, flush and start new group
grouped.append(current_group)
current_group = {
'timestamp': seg['timestamp'],
'text': seg['text'],
'speaker': speaker,
'type': 'audio'
}
# Don't forget last group
if current_group:
grouped.append(current_group)
return grouped
def format_for_claude(
self,

46
meetus/workflow.py
View File

@@ -4,6 +4,7 @@ Coordinates frame extraction, analysis, and transcript merging.
"""
from pathlib import Path
import logging
import os
import subprocess
import shutil
from typing import Dict, Any, Optional
@@ -32,6 +33,7 @@ class WorkflowConfig:
# Whisper options
self.run_whisper = kwargs.get('run_whisper', False)
self.whisper_model = kwargs.get('whisper_model', 'medium')
self.diarize = kwargs.get('diarize', False)
# Frame extraction
self.scene_detection = kwargs.get('scene_detection', False)
@@ -176,18 +178,27 @@ class ProcessingWorkflow:
if cached:
return str(cached)
# If no cache and not running whisper, use provided transcript path (if any)
if not self.config.run_whisper:
# If no cache and not running whisper/diarize, use provided transcript path (if any)
if not self.config.run_whisper and not self.config.diarize:
return self.config.transcript_path
logger.info("=" * 80)
logger.info("STEP 0: Running Whisper Transcription")
logger.info("=" * 80)
# Check if whisperx is installed
if not shutil.which("whisperx"):
logger.error("WhisperX is not installed. Install it with: pip install whisperx")
raise RuntimeError("WhisperX not installed")
# Determine which transcription tool to use
use_diarize = getattr(self.config, 'diarize', False)
if use_diarize:
if not shutil.which("whisperx"):
logger.error("WhisperX is not installed. Install it with: pip install whisperx")
raise RuntimeError("WhisperX not installed (required for --diarize)")
transcribe_cmd = "whisperx"
else:
if not shutil.which("whisper"):
logger.error("Whisper is not installed. Install it with: pip install openai-whisper")
raise RuntimeError("Whisper not installed")
transcribe_cmd = "whisper"
# Unload Ollama model to free GPU memory for Whisper (if using vision)
if self.config.use_vision:
@@ -199,21 +210,34 @@ class ProcessingWorkflow:
except Exception as e:
logger.warning(f"Could not unload Ollama model: {e}")
logger.info(f"Running WhisperX transcription with diarization (model: {self.config.whisper_model})...")
if use_diarize:
logger.info(f"Running WhisperX transcription with diarization (model: {self.config.whisper_model})...")
else:
logger.info(f"Running Whisper transcription (model: {self.config.whisper_model})...")
logger.info("This may take a few minutes depending on video length...")
# Run whisperx command with diarization
# Build command
cmd = [
"whisperx",
transcribe_cmd,
str(self.config.video_path),
"--model", self.config.whisper_model,
"--output_format", "json",
"--output_dir", str(self.output_mgr.output_dir),
"--diarize",
]
if use_diarize:
cmd.append("--diarize")
try:
subprocess.run(cmd, check=True, capture_output=True, text=True)
# Set up environment with cuDNN library path for whisperx
env = os.environ.copy()
if use_diarize:
import site
site_packages = site.getsitepackages()[0]
cudnn_path = Path(site_packages) / "nvidia" / "cudnn" / "lib"
if cudnn_path.exists():
env["LD_LIBRARY_PATH"] = str(cudnn_path) + ":" + env.get("LD_LIBRARY_PATH", "")
subprocess.run(cmd, check=True, capture_output=True, text=True, env=env)
transcript_path = self.output_mgr.get_path(f"{self.config.video_path.stem}.json")